How do local interaction patterns affect the global behavior of a community: schelling model revisited

Official URL: http://risc01.sabanciuniv.edu/record=b1817029 (Table of Contents)

In this thesis, we analyze Schelling models and propose extensions for these models. In Schelling models, two agent types (X and Y) are placed to a regular square lattice using Bernoulli distribution. Agents have their defined neighborhoods and if the percentage of the same type neighbors of an agent is smaller than its threshold, the agent is unhappy. Unhappy agents change their types and when every agent in the network is happy, the model reaches an equilibrium. Equilibrium state where agents have the minimum energy level is called ground state and we name ground state, consensus. In the square lattice, communities cannot reach a consensus (where all agents are the same type) for specific conditions. We found that changing node degrees by rewiring the links in the square lattice can help communities to reach a consensus by decreasing average shortest path length and clustering coefficient Moreover, we introduce a new agent type (XY) which represents doubtful agents and two different models (DC and PC models). In DC models, agents can give the bene t of doubt to their neighbors and communities can reach a consensus where every agent has the bene t of the doubt. On the other hand in PC models, agents cannot give the bene t of the doubt and consequently, doubtful agents disappear from the network. Lastly, we present a new approach to diversity seeking behavior where individuals seek diversity in their vicinities. We show that maze patterns emerge when agents seek diversity in their distant vicinities.